Structural Health Monitoring (SHM) is the process of monitoring the state of a structure to determine the existence, location, and degree of damage that may exist within the entire structure. A structure’s health or level of damage can be monitored by identifying changes in structural or modal parameters. In this research, the structure’s health is monitored by identifying changes in structural stiffness. The Adaptive Least Mean Square (LMS) filtering approach is used to directly identify changes in structural stiffness for the IASC-ASCE Structural Health Monitoring Task Group Benchmark problem for both Phase I and II. The research focuses primarily on Phase II of the benchmark problem. In Phase II, modeling error and noise is introduced to the problem making the problem more realistic. The research found that the LMS filter approach can be used to detect damage and distinguish relative severity of the damage in Phase II of the benchmark problem in real time. Even though the LMS filter approach identified damage, a threshold below which damage is hard to identify exists. If the overall stiffness changes less than 10%, then identifying the presence and location of damage is difficult. But if the time of damage is known, then the presence and location can be determined. The research is of great interest to those in the structural health monitoring community, structural engineers, and inspection practitioners who deal with structural damage identification problems.

Noise emission is a major drawback of the positive displacement machine. The noise source can be divided into structure borne noise source (SBNS) and fluid borne noise source (FBNS). Passive techniques such as valve plate optimization have been used for noise reduction of axial piston machines. However, passive techniques are only effective for limited operating conditions or at least need compromises in design. In this paper, active vibration control of swash plate is investigated for vibration and noise reduction over a wide range of operating conditions as an additional method to passive noise reduction techniques. A 75cc pump has been modified for implementation of active vibration control using the swash plate. One tri-axial acceleration sensor and one angle sensor are installed on the swash plate and a high speed servovalve is used for the swash plate actuation. The multi-frequency two-weight least mean square (LMS) filter synthesizes the servovalve input signal to generate a destructive interference force which minimizes the swash plate vibration. An experimental test setup has been realized using Labview field-programmable gate array (FPGA) via cRIO. Simulation and experimental studies are conducted to investigate the possibility of active vibration control.

Noise emission is a major drawback of the positive displacement machine. The noise source can be divided into structure borne noise source (SBNS) and fluid borne noise source (FBNS). Passive techniques such as valve plate optimization have been used for noise reduction of axial piston machines. However, passive techniques are only effective for limited operating conditions or at least need compromises in design. In this paper, active vibration control of swash plate is investigated for vibration and noise reduction over a wide range of operating conditions as an additional method to passive noise reduction techniques. A 75cc pump has been modified for implementation of active vibration control using the swash plate. One tri-axial acceleration sensor and one angle sensor are installed on the swash plate and a high speed servovalve is used for the swash plate actuation. The multi-frequency two-weight least mean square (LMS) filter synthesizes the servovalve input signal to generate a destructive interference force which minimizes the swash plate vibration. An experimental test setup has been realized using Labview field-programmable gate array (FPGA) via cRIO. Simulation and experimental studies are conducted to investigate the possibility of active vibration control.

This work deals with digital signal processing in the field of adaptive filtering. Fundamental basics of adaptive filtering are described and primary aim is to create executable laboratory examples, using adaptive filtering, in LabView programming language. These laboratory examples are intended to be used by students fo studying and during laboratory lessons. Objective is to connect the examples with external devices, such as microphone. A microphone is used as an user's speech input acquiring interface. In the thesis is depicted Wiener's filter and problem of adaptive filtering is discussed. Contemporary adaptive algorithms are described and their applications as well. Most mentioned is the LMS algorithm and it's forms. Laboratory examples use following concepts: Adaptive Echo Cancellation, Active Noise Control and System Identification. Each of these examples is solely executable (need for LabView or Run-time engine), consisting also of theory with diagrams. Examples therefore are usable even without manual.